A deep actor-critic reinforcement learning framework for dynamic multichannel access
To make efficient use of limited spectral resources, we in this work propose a deep actor-
critic reinforcement learning based framework for dynamic multichannel access. We …
critic reinforcement learning based framework for dynamic multichannel access. We …
Resource allocation scheme for guarantee of QoS in D2D communications using deep neural network
In this letter, we propose a hybrid resource allocation scheme for multi-channel underlay
device-to-device (D2D) communications. In our proposed scheme, the transmit power of …
device-to-device (D2D) communications. In our proposed scheme, the transmit power of …
Load-aware distributed resource allocation for MF-TDMA ad hoc networks: A multi-agent DRL approach
Without control center assistance, it is difficult that users take traffic loads of whole Ad Hoc
networks into account to utilize wireless resources collaboratively. Considering influence of …
networks into account to utilize wireless resources collaboratively. Considering influence of …
Energy-efficient mode selection and resource allocation for D2D-enabled heterogeneous networks: A deep reinforcement learning approach
T Zhang, K Zhu, J Wang - IEEE Transactions on Wireless …, 2020 - ieeexplore.ieee.org
Improving energy efficiency has shown increasing importance in designing future cellular
system. In this work, we consider the issue of energy efficiency in D2D-enabled …
system. In this work, we consider the issue of energy efficiency in D2D-enabled …
User access control in open radio access networks: A federated deep reinforcement learning approach
Targeting at implementing the next generation radio access networks (RANs) with
virtualized network components, the open RAN (O-RAN) has been regarded as a novel …
virtualized network components, the open RAN (O-RAN) has been regarded as a novel …
DDPG learning for aerial RIS-assisted MU-MISO communications
AS Abdalla, V Marojevic - 2022 IEEE 33rd Annual International …, 2022 - ieeexplore.ieee.org
This paper defines the problem of optimizing the downlink multi-user multiple input, single
output (MU-MISO) sum-rate for ground users served by an aerial reconfigurable intelligent …
output (MU-MISO) sum-rate for ground users served by an aerial reconfigurable intelligent …
Deep reinforcement learning-based spectrum allocation in integrated access and backhaul networks
We develop a framework based on deep reinforcement learning (DRL) to solve the spectrum
allocation problem in the emerging integrated access and backhaul (IAB) architecture with …
allocation problem in the emerging integrated access and backhaul (IAB) architecture with …
Distributed deep deterministic policy gradient for power allocation control in D2D-based V2V communications
Device-to-device (D2D) communication is an emerging technology in the evolution of the 5G
network enabled vehicle-to-vehicle (V2V) communications. It is a core technique for the next …
network enabled vehicle-to-vehicle (V2V) communications. It is a core technique for the next …
Autonomous power allocation based on distributed deep learning for device-to-device communication underlaying cellular network
For Device-to-device (D2D) communication of Internet-of-Things (IoT) enabled 5G system,
there is a limit to allocating resources considering a complicated interference between …
there is a limit to allocating resources considering a complicated interference between …
Deep reinforcement learning-based optimization for irs-assisted cognitive radio systems
In this paper, we consider an intelligent reflecting surface (IRS)-assisted cognitive radio
system and maximize the secondary user (SU) rate by jointly optimizing the transmit power …
system and maximize the secondary user (SU) rate by jointly optimizing the transmit power …